DESIGN, DEVELOPMENT, AND EVALUATION OF A DISCRETELY ACTUATED STEERABLE CANNULA

Needle-based procedures require the guidance of the needle to a target region to deliver therapy or to remove tissue samples for diagnosis. During needle insertion, needle deflection occurs due to needle-tissue interaction which deviates the needle from its insertion direction. Manipulating the needle at the base provides limited control over the needle trajectory after the insertion. Furthermore, some sites are inaccessible using straight-line trajectories due to delicate structures that need to be avoided. The goal of this research is to develop a discretely actuated steerable cannula to enable active trajectory corrections and achieve accurate targeting in needle-based procedures. The cannula is composed of straight segments connected by shape memory alloy (SMA) actuators and has multiple degrees-of-freedom. To control the motion of the cannula two approaches have been explored. One approach is to measure the cannula configuration directly from the imaging modality and to use this information as a feedback to control the joint motion. The second approach is a model-based controller where the strain of the SMA actuator is controlled by controlling the temperature of the SMA actuator. The constitutive model relates the stress, strain and the temperature of the SMA actuator. The uniaxial constitutive model of the SMA that describes the tensile behavior was extended to one-dimensional pure- bending case to model the phase transformation of the arc-shaped SMA wire. An experimental characterization procedure was devised to obtain the parameters of the SMA that are used in the constitutive model. Experimental results demonstrate that temperature feedback can be effectively used to control the strain of the SMA actuator and image feedback can be reliably used to control the joint motion. Using tools from differential geometry and the configuration control approach, motion planning algorithms were developed to create pre-operative plans that steer the cannula to a desired surgical site (nodule or suspicious tissue). Ultrasound-based tracking algorithms were developed to automate the needle insertion procedure using 2D ultrasound guidance. The effectiveness of the proposed in-plane and out-of-plane tracking methods were demonstrated through experiments inside tissue phantom made of gelatin and ex-vivo experiments. An optical coherence tomography probe was integrated into the cannula and in-situ microscale imaging was performed. The results demonstrate the use of the cannula as a delivery mechanism for diagnostic applications. The tools that were developed in this dissertation form the foundations of developing a complete steerable-cannula system. It is anticipated that the cannula could be used as a delivery mechanism in image-guided needle-based interventions to introduce therapeutic and diagnostic tools to a target region

Multi-Agent Ergodic Coverage with Obstacle Avoidance

Autonomous exploration and search have important applications in robotics. One interesting application is cooperative control of mobile robotic/sensor networks to achieve uniform coverage of a domain. Ergodic coverage is one solution for this problem in which control laws for the agents are derived so that the agents uniformly cover a target area while maintaining coordination with each other. Prior approaches have assumed the target regions contain no obstacles. In this work, we tackle the problem of static and dynamic obstacle avoidance while maintaining an ergodic coverage goal. We pursue a vector-field-based obstacle avoidance approach and define control laws for idealized kinematic and dynamic systems that avoid static and dynamic obstacles while maintaining ergodicity. We demonstrate this obstacle avoidance methodology via numerical simulation and show how ergodicity is maintained

Using Bayesian Optimization to Guide Probing of a Flexible Environment for Simultaneous Registration and Stiffness Mapping

One of the goals of computer-aided surgery is to match intraoperative data to preoperative images of the anatomy and add complementary information that can facilitate the task of surgical navigation. In this context, mechanical palpation can reveal critical anatomical features such as arteries and cancerous lumps which are stiffer that the surrounding tissue. This work uses position and force measurements obtained during mechanical palpation for registration and stiffness mapping. Prior approaches, including our own, exhaustively palpated the entire organ to achieve this goal. To overcome the costly palpation of the entire organ, a Bayesian optimization framework is introduced to guide the end effector to palpate stiff regions while simultaneously updating the registration of the end effector to an a priori geometric model of the organ, hence enabling the fusion of intraoperative data into the a priori model obtained through imaging. This new framework uses Gaussian processes to model the stiffness distribution and Bayesian optimization to direct where to sample next for maximum information gain. The proposed method was evaluated with experimental data obtained using a Cartesian robot interacting with a silicone organ model and an ex vivo porcine liver.</p